Predicting attitudes towards people with tuberculosis is a solution for preserving public health and a means of strengthening social ties to improve resilience to health threats. The assessment of attitudes towards the sick in general is essential to understand the educational level of a given population and to measure its resilience in contributing to the health of all within the framework of community life. The case of tuberculosis is chosen in this study to highlight the need for a change in attitudes, particularly due to the preponderance of this disease in Africa. While it is clear that attitudes influence the organization of individuals and community life, it remains a challenge to put in place an effective mechanism for evaluating the metrics that contribute to determining the attitude towards people with tuberculosis. Knowledge of attitudes towards any disease is very important to understanding collective values on this disease, hence the need to predict attitudes in the case of tuberculosis in favor of health education for all social strata while targeting areas of training not yet explored or requiring capacity building among populations. Changing attitudes towards tuberculosis patients will contribute to preserving public health and will help reduce stigma, improve understanding of the disease and encourage supportive and preventive behaviors. Achieving these changes involves dismantling stereotypes, improving access to care, mobilizing the media and social networks, including people with TB in society and strengthening the commitment of public authorities. The approach adopted consists of assessing the state of attitude towards tuberculosis patients at a given time and in a specific space based on the characteristics of the different social strata living there. An analysis of several metrics provided by machine learning algorithms makes it possible to identify differences in attitudes and serve as a decision-making aid on the strategies to be implemented. This work also relies on the investigation and analysis of historical trends using machine learning algorithms to understand population attitudes towards tuberculosis patients.

The large volume of electricity consumption data calls for the aggregation of this data. The implementation of aggregation methods is therefore a major concern to which an answer is given by presenting a case of aggregation of electricity consumption data using the jump process. A data set made it possible to carry out simulations and to present the results obtained for the daily, monthly and annual aggregations. The principle of using the jump process for the approval of these data is highlighted. This work is a concrete presentation of a simulation for the aggregation of electricity consumption data in a network of wireless sensors that can constitute a network of smart meters. The approach of this work consists in using aggregation methods to reduce the flow of data exchanges in wireless sensor networks. In fact, this work highlights several interesting properties that justify the choice of the jump process including flexibility, modeling of rare events, management of uncertainties adaptability to non-stationary data management of fluctuations in demand, consideration of volatility effects and scalability. Many significant impacts are expected, including improving network stability, optimizing resource management, reducing operational costs, integrating renewable energies, and data-driven decision-making. The jump process also presents limitations including modeling complexity, model calibration, computational complexity, interpretability of results, uncertainty management.

The smart meter can process sensor data in a residential grid. These sensors transmit different parameters or measurement data (index, power, temperature, fluctuation of voltage and electricity, etc.) to the smart meter. All of these measurement data can come in different ways at the smart meter. The sensors transmit each measurement data to the smart meter. In addition, the collection of this data to a central system is a significant concern to ensure data integrity and protect the privacy of residents. The complexity of these data management also lies in their volume, frequency, and scheduling. This work presents a scheduling and a collection mechanism in private power consumption data between both sensors and smart meters on one hand and between smart meters and the central data collection system on other hand. We have found several approaches to intelligent meter data management in scientific researches. We propose another approach in response to this concern for the scheduling and collection of measurement data to a central system from residential areas of sensors’ network connected to smart meters. This work is also an example of a link between data collection and data scheduling in intelligent information management, transmission, and protection. We also propose a modeling of the measurement objects of smart grid and highlight the changes made to these objects throughout the process of data processing. It should be noted that this smart grid system consists of three main active systems namely sensors, smart meters and central system. In addition to these three systems, there are other systems that communicate with the smart meters and the central system. We have identified three implementation models for the smart metering system. We also present an intelligent architecture based on multi-agent systems for the smart grid. Most current electricity management systems are not adapted to the new challenges imposed by social and economic development in Africa. The objectives of this study are to initiate the design of a smart grid system for the management of electricity data.